Abstract
Recognition of hand gestures is one of the most fundamental tasks in human-robot interaction. Sparse representation based methods have been widely used due to their efficiency and low demands on the training data. Recently, nonconvex regularization techniques including the l1-2 regularization have been proposed in the image processing community to promote sparsity while achieving efficient performance. In this paper, we propose a vision-based hand gesture recognition model based on the l1-2 regularization, which is solved by the alternating direction method of multipliers (ADMM). Numerical experiments on binary and gray-scale data sets have demonstrated the effectiveness of this method in identifying hand gestures.
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE International Conference on Mechatronics and Automation, ICMA 2021 |
| Pages | 187-192 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781665441001 |
| DOIs | |
| State | Published - Aug 8 2021 |
| Event | 18th IEEE International Conference on Mechatronics and Automation, ICMA 2021 - Takamatsu, Japan Duration: Aug 8 2021 → Aug 11 2021 |
Publication series
| Name | 2021 IEEE International Conference on Mechatronics and Automation, ICMA 2021 |
|---|
Conference
| Conference | 18th IEEE International Conference on Mechatronics and Automation, ICMA 2021 |
|---|---|
| Country/Territory | Japan |
| City | Takamatsu |
| Period | 8/8/21 → 8/11/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- Hand gesture recognition
- alternating direction method of multipliers
- human-robot interaction
- nonconvex regularization
- sparsity
ASJC Scopus subject areas
- Artificial Intelligence
- Electrical and Electronic Engineering
- Mechanical Engineering
- Control and Optimization